Dan Jurafsky

Visualizing and Understanding Neural Models in NLP

2016

While neural networks have been successfully applied to many NLP tasks the resulting vectorbased models are very difficult to interpret. For example it's not clear how they achieve compositionality, building sentence meaning from the meanings …

Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

2022

Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might funnel students towards a normative answer or focus students to reflect on their own thinking, deepening their understanding of …

Visualizing and Understanding Neural Models in NLP

2015 · arXiv (Cornell University)

While neural networks have been successfully applied to many NLP tasks the resulting vector-based models are very difficult to interpret. For example it's not clear how they achieve {\em compositionality}, building sentence meaning from the …

A Hierarchical Neural Autoencoder for Paragraphs and Documents

2015 · arXiv (Cornell University)

Jiwei Li, Thang Luong, Dan Jurafsky. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Lexicon-Free Conversational Speech Recognition with Neural Networks

2015

We present an approach to speech recognition that uses only a neural network to map acoustic input to characters, a character-level language model, and a beam search decoding procedure. This approach eliminates much of the …

A Simple, Fast Diverse Decoding Algorithm for Neural Generation

2016 · arXiv (Cornell University)

In this paper, we propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by adding an inter-sibling ranking penalty, favoring choosing hypotheses from diverse …

Adversarial Learning for Neural Dialogue Generation

2017 · arXiv (Cornell University)

In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the …

Data Noising as Smoothing in Neural Network Language Models

2017 · arXiv (Cornell University)

Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level …

Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction

2018

Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Ng, Dan Jurafsky. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

When Are Tree Structures Necessary for Deep Learning of Representations?

2015 · arXiv (Cornell University)

Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture. But there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In …

Neural Net Models of Open-domain Discourse Coherence

2017

Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) …

Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context

2018

We know very little about how neural language models (LM) use prior linguistic context.In this paper, we investigate the role of context in an LSTM LM, through ablation studies.Specifically, we analyze the increase in perplexity …

AI generates covertly racist decisions about people based on their dialect

2024 · Nature

Abstract Hundreds of millions of people now interact with language models, with uses ranging from help with writing 1,2 to informing hiring decisions 3 . However, these language models are known to perpetuate systematic racial …